Abstract—This research determines the influence of various

dimensions and criteria of event quality. In addressing the aim

of this study, a decision-making trial and evaluation laboratory

(DEMATEL) is employed to construct an influential network

relationship map (INRM), which then is used to illustrate the

influential network of the event quality improvement model.

The results indicated that entertainment dimension apparently

is recognized as the main key which has most influence on other

dimensions (information, transportation and environment).

This improvement model is useful in establishing an influential

network and a prioritization among dimensions/criteria

associated with event quality in tourism industry.

I. INTRODUCTION

In recent decades, events have developed significantly in

terms of number and size and have become such important

phenomena in tourism industry. Events have offered

enormous advantages to tourism industry, rapidly attracted

many visitors, facilitated recreational, social and cultural

experiences, increased media coverage and especially had

great impact on local economic [1]-[6]. Events have

stimulated economic activities, particularly increased

business turnover, created employment field and enhanced

local community income, as well as government revenue. In

other words, events are major business including huge

amounts of marketing that make many type of businesses

depend on them for their success [7]. In this context, this

success is derived from both direct and indirect large amount

of time and money spent by visitors to attend those events.

For events success are greatly affected by visitors

attendance and expenditures, one of challenges that event

provider need to thrive on is to design an unforgettable

experiences for visitors that will create loyal visitors base. In

pursuit of this stage, provider has to consistently provide a

good event quality first. For result, it will deliver outstanding

experience for visitors that will lead to satisfaction, revisit

intention and word-of-mouth. However, good event quality is

determined by many factors. Specifically, provider should

perceive and examine which factors that have most impact on

delivering a better event quality.

Manuscript received April 13, 2013; revised June 14, 2013.

Wen-Tsann Yang, Wen-Hua Liu, and Hsin-Hsien Liu are with Ph. D

Program in Civil and Hydraulic Engineering, Feng Chia University,

Taichung, Taiwan (e-mail: wtyang@fcu.edu.tw; simon3579@hotmail.com;

sunydidi@yahoo.com.tw).

Lanasari is with Suci Hati School, Jakarta, Indonesia. (e-mail:

tan.ch.ling@gmail.com).

However, when decision makers of event make decisions

from multiple factors, they must carefully deliberate many

aspects, such as dependency and conflicts among the items.

Further, each criterion has particular influence both within

dimensions and overall quality. Consequently, decision

making process becomes notably sophisticated [8], [9]. How

to construct an evaluation system of improvement priorities

is exceptionally demanding. This research applies

DEMATEL (Decision-Making Trial and Evaluation

Laboratory) method to discover the influential degree among

factors and to facilitate provider with improvement solutions

based on those priorities.

II. LITERATURE REVIEW

Event quality means product and service that delivered to

satisfy visitors‟ needs at the appropriate level. For quality

intuitively is a measure of event success in tourism and

leisure services [10], [11], hence, event providers have to set

affordable quality standards to meet visitors‟ needs.

Howard and Crompton [12] that applied Herzberg,

Mausner, and Snyderman [13] concept of leisure study,

recognized that fundamental key in visitors‟ satisfaction is

affected by the tangible physical environment (superstructure

and infrastructure) which is required for service performance

in an event [14], [15]. These tangible environmental cues are

recognized as an impulse that sway visitors‟ emotions for

what they have experienced, then for the outcome, it will

define their next behavior [16]. Further research by Brown

[17] and Crompton & Love [10] have perceived quality in the

tourism contextual as quality of performance and quality of

experience. Quality of performance (quality) is the attribute

of a service which is fully controlled by provider, while

quality of experience (satisfaction) is the visitors‟

psychological effect that turned out from accomplishment of

expectations that influenced by quality attributes and external

factors. In addition, quality can affect satisfaction and in turn

produce an impact on evaluation and improvement that will

grow number of visitors and/or revenues [18]-[22].

Accordingly, this research justify that event quality must

be take into main consideration for it directly controlled by

event provider. On the contrary, provider cannot control

psychological factors (moods, emotions, feelings) and

external factors (weather, climate). Therefore, provider can

put their attention to deliver the highest quality of

performance that they can control. For example, Baker and

Crompton [20] were determined 4 dimensions and 18

attributes of event quality: generic features, specific

entertainment features, information sources and comfort

amenities. They research indicated that quality has impact on

satisfaction and satisfaction will lead to behavior intention of

Evaluating Influential Factors in Event Quality Using

DEMATEL Method

Wen-Tsann Yang, Wen-Hua Liu, Hsin-Hsien Liu, and Lanasari

92

International Journal of Trade, Economics and Finance, Vol. 4, No. 3, June 2013

DOI: 10.7763/IJTEF.2013.V4.266

Index Terms—Event, quality, dematel (Decision-Making

Trial and Evaluation Laboratory).

visitors. Moreover, Crompton [15] was assessed six factors

of event quality. Although this research had inconclusive

results, but the assessment of those factors offered intuitive

information and helpful direction for further research.

Further, Cole and Chancellor [23] were defined 3 factors

(entertainment, amenities and programs) which consist of 15

criteria, where the result showed that entertainment had more

effects than the remaining factors. Consequently, it suggested

that provider should constantly examine and improve the

event quality based on various factors related. The previous

event studies have emphasized an evaluation system that

interprets the assessment of dimensions and criteria in this

research.

III. METHODOLOGY

The DEMATEL method which generally used to solve the

complicated and intertwined problems in MCDM is proposed

in this research to confirm the effect of each factor and

influential relationships between them.

A. Data Collection

A list of factors that related to event quality was identified

as listed in Table I. Those factors include 4 dimensions and

21 criteria. The questionnaire was designed with question

responses ranged from 0 to 4 that describe influential degree

among dimensions and criteria. In this research, the

questionnaire was filled by three groups comprised of 26

experts: nine scholars in tourism study, another eight from

government officials in various departments and the last nine

are various event providers. The ideas on the assessment

criteria were compiled through personal interview and

completed surveys which focused on event quality. Surveys

were obtained on June 2011 and each expert took about 90

minutes to be interviewed and filled the questionnaire.

B. Building a Network Relationship Using the DEMATEL

In this research, DEMATEL method is used to analyze the

interdependence of influential relationships among factors

into an intelligible structural model of system and build an

influential network relationship map (INRM). This method

uses matrix calculations to confirm all of the direct and

indirect influential relationships, as well as the impact

strength. Then, a visual structural matrix and influence

diagram is displayed to show the influential relationship and

influential degree among the dimensions and criteria in a

complex system [24]-[27]. For the result, this assists the

decision making process with a visual representation

illustrated by INRM that can be used to discover the main of

the problem and find out which factors affect each other or

themselves.

The procedures of DEMATEL method can be summarized

in the following steps:

Step 1: Find the average matrix.

Suppose Z experts and n criteria as consideration in this

research. For the kth expert, the pairwise comparisons

between criterion i affects criterion j can be denoted by k

ijx .

The evaluation score are given ranging from 0,1,2,3 and 4,

representing „no influence (0), „low influence (1)‟, „medium

influence (2)‟, „high influence (3)‟ and „very high influence

(4)‟, respectively. The scores by each experts will give the

answer of matrix kX = [k

ijx ], with 1 k Z . Then we can

compute the n x n average matrix A for all expert opinions by

averaging the Z experts‟ score as follows:

1

1 zk

ij ij

k

a xz

(1)

The average matrix ij nxn

A a is also called the initial

direct-relation matrix A, shows the initial direct effects that a

factor exerts on and receives from other factors.

Step 2: Calculate the normalized initial direct-relation matrix.

The initial direct influence matrix D (i.e., [ ]ij nxnD d )

can be derived by normalizing the average matrix A in the

following way.

/D A u (2)

1 1

1 1

max max ,maxn n

i n ij j n ij

j i

u a a

(3)

Step 3: Compute the total relation matrix

A continuous decrease of the indirect effects of problems

along the power of matrix D and the use of Markov chain

skill can gain the total relation matrix. The total relation

matrix T is an nxn matrix and is defined as follows:

2 1... ( )mT D D D D I D (4)

Then, the vector r and c can be defined sum of row and

sum of column from the matrix T by equation (5) and (6) as

following:

1

1 1

n

i ijnxj nx

r r t

(5)

'

'

11 1

n

j ijxni xn

c c t

(6)

where superscript “ ' ” denotes transpose.

TABLE I: THE DIMENSIONS AND CRITERIA ASSOCIATED WITH EVENT

QUALITY

Dimensions Criteria

Entertainment (D1) Ticket (C11)

Program performance (C12)

Varied program (C13)

Program arrangement (C14)

Food & Beverage (C15)

Souvenir (C16)

Transportation (D2) Public transport (C21)

Way to location (C22)

Parking location (C23)

Distance to location (C24)

Venue (C25)

Information (D3) Pre-information (C31)

Pamphlet (C32)

Guide (C33)

Varied information (C34)

Staff performance (C35)

Number of Staff (C36)

Environment (D4) Cleanliness (C41)

Restroom (C42)

Entrance and exit (C43)

Rest area (C44)

93

International Journal of Trade, Economics and Finance, Vol. 4, No. 3, June 2013

The ri denotes the sum of ith row in total relation matrix T.

Then it shows the total effects, both direct and indirect, given

by factor i to the other criteria. The cj means the sum of the jth

column in matrix T. The cj shows the total effects received by

criterion j from the other criteria. When j=i, the sum of

( )i ir c indicates an index representing the total effects both

given and received by criterion i. In addition, the ( )i ir c

means the net effect that criterion i contributes to the system.

When the index of ( )i ir c is positive, the criterion i is net

causer. In other words, when the index ( )i ir c is negative,

the criterion i is net receiver.

IV. ANALYSIS AND RESULTS

Every event has their uniqueness and appeal to create

interest and attract visitors‟ attention which always

demanding and expecting for more unique and engaging

experiences. The use of case study and methodology will

demonstrate how provider can enhance their effectiveness and

efficiency in producing improvement plans that will meet

their goal.

A. Problems Description

The Taipei International Expo had total attendance almost

9 million visitors and created economic benefits about

NT$ 18.8 billion. This research took this expo as an empirical

case study which designed to intend the most favorable

improvement plan for event provider. Event quality is

determined by many factors related. The purpose of this

research is to help provider in decision making process in

quality improvement. Provider can find out the influential

relationships between dimensions and criteria associated with

event quality by using DEMATEL method, and in turn

design the improvement strategy based on priority sequences

among those dimensions and criteria.

B. Construct INRM Using DEMATEL

Based on the questionnaires that filled by 26 experts, we

define the initial direct-relation matrix and further

normalized as the matrix D (as shown in Table II) using

DEMATEL, then calculate the total relation matrix T of the

criteria (Table III). The consistency test with a high

consistency ratio of 95.26% indicates that the result is

significantly dependable. According to the values of ( )i ir c

and ( )i ir c , the results for the dimensions and criteria are

shown in Table IV. This result allows us to construct INRM

as illustrated in Figure 1. When ( )i ir c is high and ( )i ir c

is positive, it means that the criterion has most influence and

be a primary key to be concerned about. In contrast, when

( )i ir c is high and ( )i ir c is negative, it means that the

criterion is highly significant to be influenced by other

criteria.

TABLE II: NORMALIZED MATRIX D

Note:

1

21 1

1100% 4.74% 5%

p pn n

ij ij

pi j

ij

t tx

n t

, consistency ratio 95.26% denote the average influence of i criterion to j.

The results displayed that entertainment (D1) has the

highest influence level that impact remaining dimensions

directly. Additionally, environment (D4) has the lowest

influential degree and is the most vulnerable to impact other

dimensions. As presented in Table III, the prioritization of

influential degree can be sequenced as: D1 _ D2 _ D3 _ D4.

Regarding the aim of improvement, the first priority to be

improved should be entertainment (D1), for it can bring out

influential impact to other dimensions: transportation (D2),

information (D3) and environment (D4). Hence, provider

should manage their concern to this standpoint. In addition,

experts in event studies clarify that entertainment has most

influential impact on an event and improvement of this

dimension will bring out an influence on remaining

dimensions as well. The influential relationship also can be

seen within each dimension and criteria. As an example,

within the dimensions of entertainment (D1), it can be

identified that varied program (C13) affected the remaining

criteria. Conversely, souvenir (C16) is accepted the most

influence from other criteria. This result supports previous

research which revealed that the event program significantly

drove out satisfaction and influenced visitors to attend an

event [7], [28]. Taken as a whole, the particular order of

general improvement priority for entertainment (D1) is:

94

International Journal of Trade, Economics and Finance, Vol. 4, No. 3, June 2013

(C13)_(C14)_(C12)_(C11)_(C15)_(C16). Similar influential

relationships also can be defined for the remaining criteria

within other dimensions, as illustrated in detail in Fig. 1. This

method is a helpful tool for decision-making process in

identifying priority for improvement strategy of event

quality.

TABLE III: TOTAL RELATION MATRIX T

Fig. 1. Influential Network Relationships Map

TABLE IV: RESULTS OF DIMENSIONS / CRITERIA ANALYSIS

Dimensions / Criteria r c r + c r - c

Entertainment (D1) 0.5824 0.4459 1.0284 0.1365

Ticket (C11) 0.8964 0.9167 1.8131 -0.0204

Program performance (C12) 0.8285 0.7665 1.5949 0.0620

Varied program (C13) 0.9740 0.7951 1.7691 0.1788

Program arrangement (C14) 0.9389 0.8652 1.8040 0.0737

Food and beverages (C15) 0.5514 0.6863 1.2377 -0.1349

Souvenir (C16) 0.4501 0.6093 1.0593 -0.1592

Transportation (D2) 0.5448 0.5736 1.1185 -0.0288

Public transport (C21) 0.7228 0.8583 1.5811 -0.1356

Way to location (C22) 0.8187 0.8760 1.6947 -0.0574

Parking location (C23) 0.7976 0.7986 1.5962 -0.0010

Distance to location (C24) 0.9039 0.8126 1.7165 0.0913

Venue (C25) 0.8207 0.7181 1.5388 0.1026

Information (D3) 0.5113 0.5635 1.0748 -0.0522

Pre-information (C31) 0.9346 0.9327 1.8673 0.0019

Pamphlet and signage (C32) 0.9669 0.8080 1.7749 0.1590

Guide (C33) 0.8384 0.7984 1.6368 0.0400

Varied information (C34) 0.8223 0.9298 1.7521 -0.1076

Staff performance (C35) 0.7856 0.8187 1.6043 -0.0331

Number of Staff (C36) 0.7864 0.8466 1.6330 -0.0602

95

International Journal of Trade, Economics and Finance, Vol. 4, No. 3, June 2013

Environment (D4) 0.4212 0.4767 0.8978 -0.0555

Cleanliness (C41) 0.3980 0.5072 0.9052 -0.1091

Restroom (C42) 0.4236 0.4347 0.8583 -0.0111

Entrance and exit (C43) 0.4304 0.3268 0.7572 0.1036

Rest area (C44) 0.3981 0.3815 0.7796 0.0166

V. CONCLUSION

At present, event providers constantly put efforts in

providing satisfactory environment for visitors. When

provider can deliver more satisfying product and service

quality, then they will be capable taking lead among

competitive circumstances in event industries. However,

different types or characteristics of events including the

factors of event quality may or may not same, but those

factors are various and complicated. So, the methodologies

should be taken as consideration in order to achieve the goal.

Getz [29] noted that the approaches from various research

methodologies are found to be useful in establishing event

structure. This research applied DEMATEL method to

construct an influence relationships system, which can assist

decision makers to directly evaluate and improve factors of

events according to their influential degree.

However, the results of this research may not be

generalized to other types of events because probably there is

sampling error in one sampling event. Future research may

take other objects, industry fields or methodologies for

gaining knowledge, obtaining useful insights and advancing

concepts in event studies. The application of DEMATEL can

support provider to discover the prioritization of event

quality improvement and direct their attention to create a

better event quality.

REFERENCES

[1] D. Getz, Festivals, Special Events, in Khan, M. A., Olsen, M. D. and T.

Var, (Eds), Encyclopedia of Hospitality and Tourism, Van Nostrand

Reinhold, New York, NY, 1993, pp. 789-810.

[2] J. L. Crompton and S. L. McKay, “Measuring the economic impacts of

festivals and events: Some myths, misapplications and ethical

dilemmas,” Festival Management & Event Tourism, vol. 2, no.1, pp.

33–43, 1994.

[3] D. Getz, Event Management and Event Tourism, New York: Cognizant

Communication, 1997.

[4] D. Felsenstein and A. Fleischer, “Local Festivals and Tourism

Promotion: The Role of Public Assistance and Visitor Expenditure,”

Journal of Travel Research, vol. 41, no.4, pp. 385-392, 2003.

[5] C. Lee, Y. Lee, and B. Wicks, “Segmentation of festival motivation by

nationality and satisfaction,” Tourism Management, vol. 25, no. 1, pp.

61-70, 2004.

[6] B. McKercher, W. Mei, and T. Tse, “Are short duration festivals tourist

attractions?” Journal of Sustainable Tourism, vol. 14, no. 1, pp. 55-66,

2006.

[7] Y. Lee, C. Lee, S. Lee, and B. Babin, “Festivalscapes and patrons‟

emotions, satisfaction and loyalty,” Journal of Business Research, vol.

61, no. 1, pp. 56-64, 2008.

[8] Y. W. Chen, and G. H. Tzeng, “Using fuzzy integral for evaluating

subjectively perceived travel costs in a traffic assignment model”,

European Journal of Operational Research, vol. 130, pp. 653–664,

2001.

[9] G. H. Tzeng and J. J. Huang, Multiple Attribute Decision Making:

Methods and application, New York: CRC Press, Taylor & Francis

publishing group, 2011.

[10] J. L. Crompton and L. Love, “The Predictive Validity of Alternative

Approaches to Evaluating Quality of a Festival,” Journal of Travel

Research, vol. 34, no. 1, pp. 11-24, 1995.

[11] D. Getz, M. O‟Neill, and J. Carlsen, “Service Quality Evaluation at

Events through Service Mapping,” Journal of Travel Research, vol. 39,

pp. 380-390, 2001.

[12] D. R. Howard, and J. L. Crompton, Financing, Managing, and

Marketing Recreation and Park Resources, Wm. C. Brown Company,

Dubuque, Iowa, 1980.

[13] F. Herzberg, B. Mausner, and B. B. Snyderman, The motivation to

work, New. York: Wiley, 1959.

[14] M. Bitner, “Evaluating Service Encounters: The Effects of Physical

Surroundings and Employee Responses,” Journal of Marketing, vol.

54, April, pp. 69-82, 1990.

[15] J. L. Crompton, “Adapting Herzberg: A Conceptualization of the

Effects of Hygiene and Motivator Attributes on Perceptions of Event

Quality,” Journal of Travel Research, vol. 41, pp. 305-310, 2003.

[16] A. Mehrabian and J. Russell, An Approach to Environmental

Psychology, Cambridge, Mass.: MIT Press, 1974.

[17] P. J. Brown, “Quality in Recreation Experience, in Outdoor Recreation

Benchmark 1988: Proceedings of the National Recreation Forum, A.

H. Watson (ed.), Tampa, Florida, USDA Forest Service, General

Technical Report SE-52, 1988.

[18] J. L. Crompton and S. L. McKay, “Motives of visitors attending

festival events,” Annals of Tourism Research, vol. 6, no. 4, pp. 425-39,

1997.

[19] R. D. Childress and J. L. Crompton, “A comparison of alternative

direct and discrepancy approaches to measuring quality of performance

at a festival,” Journal of Tourism Research,, vol. 36, no. 2, pp. 43-57,

1997.

[20] D. A. Baker and J. L. Crompton, “Quality, satisfaction and behavioral

intentions,” Annals of Tourism Research, vol. 27, no. 3, pp. 785-804,

2000.

[21] S. T. Cole and J. L. Crompton, “A conceptualization of the

relationships between service quality and visitor satisfaction, and their

links to destination selection,” Leisure Studies, vol. 22, pp. 65-80,

2003.

[22] Y. S. Yoon, J. S. Lee, and C. K. Lee, “Measuring festival quality and

value affecting visitors‟ satisfaction and loyalty using a structural

approach,” International Journal of Hospitality Management, vol. 29

pp. 335–342, 2010.

[23] S. T. Cole and H. C. Chancellor, “Examining the festival attributes that

impact visitor experience, satisfaction and re-visit intention,” Journal

of Vacation Marketing, vol. 15, pp. 323-333, 2009.

[24] V. Y. C. Chen, H. P. Lien, C. H. Liu, J. J. H. Liou, G. H. Tzeng, and L.

S. Lang, “Fuzzy MCDM approach for selecting the best

environment-watershed plan,” Applied soft computing, vol. 11, pp.

265-275, 2011.

[25] C. H. Huang, G. H. Tzeng, and W. R. Ho, “System on chip design

service e-business value maximization through a novel MCDM

framework,” Expert Systems with Applications, vol. 38, pp. 7947-7962,

2011.

[26] C. Y. Huang, J. Z. Shyu, and G. H. Tzeng, “Reconfiguring the

innovation policyportfolios for Taiwan‟s SIP mall industry,”

Technovation, vol. 27, pp. 744-765, 2007.

[27] J. L. Yang and G. H. Tzeng, “An integrated MCDM technique

combined with DEMATEL for a novel cluster-weighted with ANP

method,” Expert Systems with Applications, vol. 38, pp. 1417-1424,

2011.

[28] F. Saleh and C. Ryan, “Jazz And Knitwear: Factors That Attract

Tourists To Festivals,” Tourism Management, vol. 14, no. 4, pp.

289-297, 1993.

[29] D. Getz, “Event tourism: Definition, evolution, and research,” Tourism

Management, vol. 29, pp. 403–428, 2008.

Wen-Tsann Yang was born in Taipei, Taiwan on

December 9th, 1955. He finished his M.S degree of

Forestry from University of Montana, Missoula,

Montana, USA in 1984, and earned Ph. D degree of

Geography from Southern Illinois University,

Carbondale, Illinois, USA in 1992. After his study in

USA, he went back to Taiwan to serve as an associate

professor in Department of Land management at Feng

Chia University, Taichung, Taiwan. He has been

teaching some courses such as: Environment and Ecology, Commercial

Recreation Management in undergraduate program, and Recreation Theories,

Graduate Seminar and Conflict Management in graduate program for more

than 20 years. Besides teaching, he has continued conducting research

financially supported by National Science Council in Taiwan. The major

study topics including: visitors‟ littering behaviors in recreation areas,

ecotourism planning and management in aboriginal tribal areas, sexual

leisure in metropolitan areas, and so on.

He was a co-author of a book with other 19 authors, titled as “Leisure and

Recreation: Theories and Practices”, edited by Dr. Ou with 1st edition in

2007 and 2nd edition in 2011, respectively published by Chien-Tsen Cultural

Business Co. New Taipei City, Taiwan. Various articles based on his studies

96

International Journal of Trade, Economics and Finance, Vol. 4, No. 3, June 2013

were published both in different journals and conference proceedings. The

future study in which he is interested would be topics relevant to emotional

labor in service business, such as managers of recreation areas, real estate

agents and/or tour guides.

Dr. Yang has been serving as executive councilor in the board of Outdoor

Recreation Association in Taiwan, Member of the Evaluation Team in

Taiwan Tourism Bureau, Paper Reviewer of Journal of Outdoor Recreation

Studies in Taiwan, and so on for many years. In addition, he won Excellent

Teacher Award from Feng Chia University, Taichung Taiwan in 1997,

Contribution Award 2011 and 2012 from Overseas Mandarin Council in

Taiwan.

Wen-Hua Liu was born in Taipei, Taiwan on April 25th,

1969. He has a master‟s degree of tourism management

from Chinese Culture University, Taipei, Taiwan in

2008. He is currently a third year PhD student at Feng

Chia University, Taichung, Taiwan in Civil and

Hydraulic Engineering with resource management as

major field of study.

He started his own business in human resources and

travel services industries. Besides, he has been teaching

Tourism English at Taipei College of Maritime Technology for the past five

years, and has been actively teaching Travel and Industry Management at

Feng Chia University, Taichung, Taiwan since 2012.

He has published several papers based on the Ph. D research which focus

on fields of tourism and recreation, as well as other fields related to resource

management. His research interests are including tourism management and

MCDM (Multiple Criteria Decision Making) methods.

Hsin-Hsien Liu was born in Taichung, Taiwan on

October 8th, 1981. He was graduated from Feng Chia

University Ph. D. program in Civil and Hydraulic

Engineering, Taichung, Taiwan in 2012, with the

railway industry reform as major field of study.

He had worked as research assistant in Research

Center of Traffic Accident Appraisal (RCTAA) and

Advanced Traffic Management Center in Feng Chia

University, Taichung Taiwan during 2006-2012. He

also has been teaching Transportation data analysis and Transportation

decision making analysis during 2011-2012 at Department of Transportation

Technology and Management, Feng-Chia University, Taichung, Taiwan.

Currently, he has been serving in the army to be the Substitute Military

Service since December 2012.

He has published several papers which about transportation safety and

railway operation used data mining and MCDM methods. His research

interests contain railway industry operation and reform, traffic accident

safety and analysis, multiple criteria decision making and data mining.

Lanasari was born in Jakarta, Indonesia on March 13th,

1985. She was graduated from SMUK Kalam Kudus I

Senior High School, Jakarta, Indonesia, in 2002, major

in science. She was studied abroad at Feng Chia

University, Taichung, Taiwan in 2012, with Tourism

and Hospitality Management as major field of study.

She had worked as Finance and Accounting Staff at

a company in Jakarta, Indonesia for five years. She also

has been teaching since 2008 at Suci Hati School,

Jakarta, Indonesia, responsible for educating junior

high school students in several subjects (Mathematic, Science and Chinese

language).

She is interested in social sciences, statistic study and research

methodologies, such as MCDM (Multiple Criteria Decision Making)

methods.

97

International Journal of Trade, Economics and Finance, Vol. 4, No. 3, June 2013